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Machine Learning Techniques—Reductions Between Prediction Quality Metrics

  • Alina Beygelzimer
  • John Langford
  • Bianca Zadrozny

Machine learning involves optimizing a loss function on unlabeled data points given examples of labeled data points, where the loss function measures the performance of a learning algorithm. We give an overview of techniques, called reductions, for converting a problem of minimizing one loss function into a problem of minimizing another, simpler loss function. This tutorial discusses how to create robust reductions that perform well in practice. The reductions discussed here can be used to solve any supervised learning problem with a standard binary classification or regression algorithm available in any machine learning toolkit. We also discuss common design flaws in folklore reductions.

Keywords

Loss Function Fault Diagnosis Quantile Regression Machine Learn Technique Neural Information Processing System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Alina Beygelzimer
    • 1
  • John Langford
    • 2
  • Bianca Zadrozny
    • 3
  1. 1.IBM Thomas J. Watson Research CenterHawthorne
  2. 2.Yahoo! ResearchNew York
  3. 3.Fluminense Federal UniversityBrazil

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